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首页> 外文期刊>Journal of Nondestructive Evaluation >Approximate Inverse Mapping in ECT, Based on Aperture Shifting and Neural Network Regression
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Approximate Inverse Mapping in ECT, Based on Aperture Shifting and Neural Network Regression

机译：基于孔径移位和神经网络回归，在ect中近似逆映射

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The inversion procedure presented in this paper is based on the statistical regression of the inverse map between the spaces of ECT scan data, and of crack parameters. The mapping is realized by a combination between a statistical data processing step, i.e., a principal component transformation of the scan data, and an incremental resolution neural network training. Starting from the necessities of improving the detrimental conditioning of the regression and of providing the inversion approach with enhanced potential for automation, an novel "shifting aperture" mapping concept and a data fusion technique are proposed. Supplementing the primary mapping algorithm with these latter processing step allows one to avoid the usual anomalous-region focusing approach and improves the inversion capabilities by allowing a dynamic reconstruction of the object's profile. Unconnected and multiply connected crack shapes are well estimated, that so far eluded most other inversion methods. For this primary validation of the completed algorithm, only synthetic B-scan data are used, which are collected by an optimized, high performance sensor on the interior of a metal tube.
机译：本文呈现的反转过程基于ECT扫描数据的空间与裂纹参数之间的逆映射的统计回归。通过统计数据处理步骤，即扫描数据的主要组件变换和增量分辨率神经网络训练之间的组合来实现映射。从改善回归有害调节的必要性以及提供具有增强自动化电位的反演方法的必要性，提出了一种新颖的“移位孔径”映射概念和数据融合技术。补充具有这些后处理步骤的主映射算法允许人们避免通常的异常区域聚焦方法，并通过允许对象的配置文件的动态重建来提高反转能力。估计未连接和乘以连接的裂纹形状，即到目前为止突出了大多数其他反演方法。对于完成算法的这种主要验证，仅使用合成B扫描数据，其通过金属管内部的优化的高性能传感器收集。

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